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Analyzing Factors, Construction of Dataset, Estimating Importance of Factor, and Generation of Association Rules for Indian Road Accident

机译:分析因素,数据集建设,估算因子的重要性,以及印度道路事故结社的生成

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Convenience and safety of road is very important and crucial in society. Statistics of accidental deaths has shown a growing drift in India. Accidents on road network with vehicle collision can lead to injuries or even deaths and causes economy loss. Reasons for accident includes over speeding, drunken driver and defects in vehicle as well as bad road condition. Places near residential areas, pedestrian crossing, around schools, college and other educational institutions are the major zone of accidents. Mega cities traffic in India is increasing day by day due to increase in the population. In this paper we have figure out the factor influencing the accidents and identified which factor is more accident prone with Info Gain Attribute Evaluator function using WEKA tool. We have also performed association classification using Apriori algorithm and identified best rule for accident dataset.
机译:道路的便利性和安全性在社会中非常重要和至关重要。意外死亡的统计数据显示在印度越来越漂移。带有车辆碰撞的道路网的事故可能导致伤害甚至死亡,并导致经济损失。事故的原因包括超速,醉酒的驱动器和车辆的缺陷以及不良的道路状况。住宅区附近的地方,行人横穿,学校,学校,学院和其他教育机构是事故的主要区域。由于人口的增加,印度的巨型城市交通日益增加。在本文中,我们已经弄清楚了影响事故的因素,并确定了使用Weka工具对信息GAIN属性评估函数易于出发的因素。我们还使用APRIORI算法执行关联分类,并确定了事故数据集的最佳规则。

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